In big data interoperability scenarios, single privacy protection technologies struggle to balance the demands of efficient data processing with stringent privacy security requirements. Homomorphic encryption and secure multi-party computation, two core technologies in privacy computing, enable data to be “both usable and invisible.” However, these technologies are often applied independently or only superficially integrated, exhibiting common limitations such as high computational overhead, poor cross-platform interface compatibility, a lack of a unified protection framework, and limited general adaptability. This paper systematically analyzes the fundamental theories and operational characteristics of both technologies, identifies key challenges in their integration, and proposes a unified privacy protection architecture. The architecture optimizes hybrid algorithm operation modes and comprehensive permission management mechanisms while establishing lightweight, standardized, and dynamically adjustable optimization strategies. Key contributions include: developing a universal collaborative protection framework applicable to government, financial, and healthcare applications; overcoming the limitations of traditional single-technology approaches and superficial integration methods; achieving an effective balance between data privacy security and collaborative computing efficiency through optimized algorithms and standardized interfaces; filling research gaps in deeply integrated standardization frameworks for these technologies; and providing theoretical foundations and practical references for large-scale implementation of compliant cross-domain data sharing and privacy protection solutions.